Continuous PID Sliding Mode Control Based on Neural Third Order Sliding Mode Observer for Robotic Manipulators

This paper proposes a continuous PID sliding mode control strategy based on a neural third-order sliding mode observer for robotic manipulators by using only position measurement. A neural third-order sliding mode observer based on radial basis function neural network is first proposed to estimate both the velocities and the dynamic uncertainties and faults. In this observer, the radial basis function neural networks are used to estimate the parameters of the observer, therefore, the requirement of prior knowledge of the dynamic uncertainties and faults is eliminated. The obtained velocities and lumped uncertainties and fault information are then employed to design the continuous PID sliding mode controller based on the super-twisting algorithm. Consequently, this controller provides finite-time convergence, high accuracy, chattering reduction, and robustness against the dynamic uncertainties and faults without the need of velocity measurement and the prior knowledge of the lumped dynamic uncertainties and faults. The global stability and finite-time convergence of the controller are guaranteed in theory by using Lyapunov function. The effectiveness of the proposed method is verified by computer simulation for a PUMA560 robot.